Add label smoothing param in DiceCELoss (Project-MONAI#8000)

Fixes Project-MONAI#7957 

### Description
In this modified version I made the following changes:
1. Added `label_smoothing: float = 0.0` parameter in `__init__` method,
default value is 0.0.
2. When creating the `self.cross_entropy` instance, pass the
`label_smoothing` parameter to `nn.CrossEntropyLoss`.
3. Added `self.label_smoothing = label_smoothing` in the `__init__`
method to save this parameter for access when needed.

For example:
```
from monai.losses import DiceCELoss

# Before
criterion = DiceCELoss()
criterion.cross_entropy.label_smoothing = 0.1

# Now
criterion = DiceCELoss(label_smoothing=0.1)
```

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [ ] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: ytl0623 <david89062388@gmail.com>
Co-authored-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>

monai/losses/dice.py

@@ -666,6 +666,7 @@ def __init__(
         weight: torch.Tensor | None = None,
         lambda_dice: float = 1.0,
         lambda_ce: float = 1.0,
+        label_smoothing: float = 0.0,
     ) -> None:
         """
         Args:
@@ -704,6 +705,9 @@ def __init__(
                 Defaults to 1.0.
             lambda_ce: the trade-off weight value for cross entropy loss. The value should be no less than 0.0.
                 Defaults to 1.0.
+            label_smoothing: a value in [0, 1] range. If > 0, the labels are smoothed
+                by the given factor to reduce overfitting.
+                Defaults to 0.0.
 
         """
         super().__init__()
@@ -728,7 +732,12 @@ def __init__(
             batch=batch,
             weight=dice_weight,
         )
-        self.cross_entropy = nn.CrossEntropyLoss(weight=weight, reduction=reduction)
+        if pytorch_after(1, 10):
+            self.cross_entropy = nn.CrossEntropyLoss(
+                weight=weight, reduction=reduction, label_smoothing=label_smoothing
+            )
+        else:
+            self.cross_entropy = nn.CrossEntropyLoss(weight=weight, reduction=reduction)
         self.binary_cross_entropy = nn.BCEWithLogitsLoss(pos_weight=weight, reduction=reduction)
         if lambda_dice < 0.0:
             raise ValueError("lambda_dice should be no less than 0.0.")